Effectiveness of Digital Storefront Information on Consumer Choice

Last registered on October 17, 2022

View Trial History

Pre-Trial

Trial Information

General Information

Title

Effectiveness of Digital Storefront Information on Consumer Choice

RCT ID

AEARCTR-0010119

Initial registration date

October 10, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

October 17, 2022, 4:04 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

United States of America

Region

Primary Investigator

Name

Yash Babar

Affiliation

UW-Madison

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2022-10-15

End date

2022-12-15

Keywords

Behavior

Additional Keywords

Information Systems, Social Identity

JEL code(s)

M20, M31, Z13, J15

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

The purpose of the research is to understand how people consume information from an online places business profile page while choosing a place to dine or drink at. By manipulating specific components of a digital store front, the study aims to understand the marginal influence of each on consumers' subsequent willingness to visit the business and associated perceptions of value, expectations of the experience, and other related outcomes.

External Link(s)

Registration Citation

Citation

Babar, Yash. 2022. "Effectiveness of Digital Storefront Information on Consumer Choice." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.10119-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

All subjects shall be presented with different variations of digital storefronts mimicking real business locations from a large Midwestern city. Subjects shall be randomly shown different permutations of manipulations involving one or more attributes of the business. After each such image of a storefront, subjects will be asked to rate their likelihood of visiting the business, and their perceptions of its value to them and to others.

Intervention Start Date

2022-10-15

Intervention End Date

2022-12-15

Primary Outcomes

Primary Outcomes (end points)

Consumer willingness to physically visit a store after being presented with different information via a digital storefront profile.

Primary Outcomes (explanation)

Willingness to visit the store/restaurant shall be measured on a 1-5 Likert scale.

Secondary Outcomes

Secondary Outcomes (end points)

Consumer perceptions of others' willingness to visit each place, and their perceptions about expectations of food, service and atmosphere of each food and drink establishment.

Secondary Outcomes (explanation)

Each of the secondary outcomes will be measured on a 1-5 Likert scale.

Experimental Design

Individuals will be shown 16 digital storefront profiles with manipulated store attributes, their willingness to visit the store and general perceptions of it in response to said information are noted for all the places. The study is implemented via a Qualtrics survey and each individual is shown a randomized set of places with randomized store attributes. Each individual can potentially be exposed to all treatment conditions (but only one treatment per unique storefront location).

Experimental Design Details

Out of 80 possible Google Places profiles, each subject shall be shown a random set of 16 places. For each place, manipulations for one of the four possible conditions (1 control and 3 treatments) shall be randomly shown. In this manner, each subject can be potentially exposed to all treatment conditions but not for the same place and each place in turn can also be manipulated in all different treatment arms but not for the same subject.

Randomization Method

Randomization is implemented via an online Qualtrics survey.

Randomization Unit

Individuals; within-subject design with each individual making multiple choices and seeing multiple treatment arms.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

1500 participants, not clustered.

Sample size: planned number of observations

24,000 choices, 16 each for 1,500 participants

Sample size (or number of clusters) by treatment arms

Each of the 1500 individuals will be shown 16 options drawn randomly from one of four conditions (a two factorial design). Hence, roughly 6000 observations per condition are expected.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Assuming 6000 observations, a SD of 0.75 at 80% power on a 5 point Likert scale and an alpha of 0.05 we expect to be able to detect effects as small as 0.038. Based on our calculations from observational data measuring the impact of a single treatment on actual store visitation we expect to see effects with a Cohen's d = 0.4 (or a medium sized effect).

Supporting Documents and Materials

IRB